In signal processing applications that use detector networks for data input, a common problem is random detector output errors that affect the reliability of detector data input to the processing system. Detector output over time is affected by such factors as operating environment, structural materials and fabrication processes. Resulting detector output errors tend to be nonuniform, and even more problematic, tend to change unpredictably with changes in operating environment and duty cycle.
An example of a system that processes data generated by a detector network is a thermal imaging system, which includes a thermal detection subsystem with a network of thermal radiation detectors (such as a focal plane array). The detector array is typically scanned across the image scene, and incident thermal radiation is gathered by the detectors and converted by the thermal detection subsystem into digital detector output samples that comprise the pixels (picture elements) of the image scene. Because of nonuniformities in both structural materials and fabrication processes, the detector-to-detector response to a given level of incident thermal radiation is nonuniform.
The conventional approach to increasing the accuracy of detector output data is to calibrate detector response by calibrating, and periodically recalibrating, the detector network. While the signal processing system is off-line, a reference is introduced--such as a thermal reference source in the case of the exemplary thermal imaging system--and the response of each detector in the detector network to the reference is recorded. From these detector reference responses, a detector calibration error representative of deviation in detector reference response from the ideal can be computed for each detector of the detector network, and used during on-line processing to correct detector output.
While the calibration correction for each detector will be different because of nonuniformities in detector-to-detector response to the reference, the reference output error for each detector can be characterized by a first order linear function m(x +b), where m is a gain factor, and b is an offset level. That is, for each detector, detector response to a reference can be used to compute calibration gain (m) and offset (b) coefficients for correcting detector output. These calibration coefficients are then used to correct detector output errors during on-line operation.
A problem with this calibration approach is that detector output changes unpredictably as a function of time and operating environment. As a result, the calibration coefficients computed during a calibration procedure become less representative of actual detector output errors, and therefore, less able to provide adequate compensation. Thus, while regular recalibration can be used to compensate for static performance deviations (such as caused by structural changes or materials degradation), achieving continuously accurate data from a periodically recalibrated detector network is made problematic by dynamic operational nonuniformities in detector response.
An additional problem with detector output accuracy is the limit on precision conventionally obtainable from detector networks. That is, even without the problem of detector output errors due to dynamic nonuniform changes in detector performance, many conventional detector networks can not provide data with the level of precision that the associated signal processing systems are capable of handling. This limit on the precision of detector data output effectively limits signal processing accuracy and flexibility.
In the case of the exemplary thermal imaging system, current thermal detector technology typically limits precision to 12 bits--beyond that level, detector noise resulting from random nonlinearities and nonuniformities become significant in terms of limiting the precision of the detector output signal. However, these thermal imaging systems typically incorporate the image processing capability to handle at least 16-bit precision.
Accordingly, a need exists for a detector normalization circuit that normalizes detector output using dynamically updated gain and offset normalization coefficients, maintaining detector output accuracy over a relatively long period of time under a variety of operating conditions. Preferably, such a detector normalization system would provide means to increase the precision of the detector output data.